Alternative Procedure for Determination of Hot Mix Asphalt Creep Compliance
Bibliographic record
Abstract
Abstract Creep compliance function is one of the fundamental properties of viscoelastic materials. In asphalt research, creep compliance is used in the prediction of low-temperature cracking, which is a prevalent distress in asphalt pavements in northern parts of the United States and Canada. The thermal cracking prediction model included in the current version of the Mechanistic-Empirical Pavement Design Guide requires the creep compliance of hot mix asphalt (HMA) materials as a primary input. The current AASHTO procedure for determining creep compliance of HMA is based on the indirect tension (IDT) test. The IDT test is performed on relatively thick cylindrical specimens, which makes this test unsuitable for field cores from thin layers and construction lifts. In addition, thick specimens do not allow for investigation of the gradual aging that occurs in asphalt layers due to oxidation and volatilization. This paper outlines the procedure that uses the bending beam rheometer (BBR) as an alternative to the IDT test to determine the low-temperature creep compliance of HMA using thin beam specimens. Twenty different HMA mixtures are tested at three temperature levels using both the IDT and the BBR instruments. The analysis of test results is presented in two parts. First, the variability in creep compliance values obtained from the IDT and the BBR tests is discussed. Next, the artificial neural networks (ANNs) are trained to (1) predict IDT results from BBR measurements using design parameters of HMA mixture and testing temperature as the model inputs and (2) backcalculate HMA creep compliance from the binder creep compliance and vice versa. It is concluded that testing HMA beams can be used instead of IDT specimens for the low-temperature characterization of HMA mixtures, and the ANN can be successfully used for the backcalculation of the creep compliance measured by both IDT and BBR devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".